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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1676–1700 of 2050 papers

TitleStatusHype
mlr3summary: Concise and interpretable summaries for machine learning modelsCode0
Can LLMs Predict Citation Intent? An Experimental Analysis of In-context Learning and Fine-tuning on Open LLMsCode0
Model Assessment and Selection under Temporal Distribution ShiftCode0
Fair Enough: Standardizing Evaluation and Model Selection for Fairness Research in NLPCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
Structured Variational Learning of Bayesian Neural Networks with Horseshoe PriorsCode0
Fairness and bias correction in machine learning for depression prediction: results from four study populationsCode0
Model-based Clustering using Automatic Differentiation: Confronting Misspecification and High-Dimensional DataCode0
Familia: An Open-Source Toolkit for Industrial Topic ModelingCode0
Entity Set Search of Scientific Literature: An Unsupervised Ranking ApproachCode0
Fast and Informative Model Selection using Learning Curve Cross-ValidationCode0
Automatic Catalog of RRLyrae from 14 million VVV Light Curves: How far can we go with traditional machine-learning?Code0
Adaptive multi-penalty regularization based on a generalized Lasso pathCode0
Model Evaluation, Model Selection, and Algorithm Selection in Machine LearningCode0
Fast Cross-Validation via Sequential TestingCode0
Fast Instrument Learning with Faster RatesCode0
Parameter identifiability and model selection for partial differential equation models of cell invasionCode0
Topological Data Analysis of Decision Boundaries with Application to Model SelectionCode0
Automatic AI Model Selection for Wireless Systems: Online Learning via Digital TwinningCode0
Optimal design of experiments to identify latent behavioral typesCode0
PARAPHRASUS : A Comprehensive Benchmark for Evaluating Paraphrase Detection ModelsCode0
Modeling High-Dimensional Data with Unknown Cut Points: A Fusion Penalized Logistic Threshold RegressionCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
pared: Model selection using multi-objective optimizationCode0
Pareto-optimal clustering with the primal deterministic information bottleneckCode0
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